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FIN40090 Stacked Ensembling
创建日期：2024-04-08 16:09:25
标签： FIN40090
Stacked Ensembling

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FIN40090 Stacked Ensembling and Algorithmic Trading Project (Group) 

The purpose of this project is to bring together the methods we have had so far this semester to produce a forecast of (‘trimmed’) 

daily return of a relatively large Equity or major Equity Index, and to carry out a study of simulated trading which includes at least 250 days of ‘out-of-sample’ performance. 

The study should be documented and presented in the form of a report 

(single pdf file, no longer than 15 pages, including printout and tables) 

and a summary presentation of 5 minutes (plus time for questions) to be delivered jointly by group members in the penultimate class 

[the presentation part may be pre-recorded]. Specifically, the steps included are as follows: 1. Agree (and submit) one choice of 

Equity and one of Equity Index (other than FTSE) for your group to work on. You will be notified which of these your group will be assigned to work on. 

2. Prepare input and output data matrices of lagged daily (‘trimmed’) logarithmic return, removing non-trading (exactly zero-return output) days as

 shown in class. [In addition to lagged return information, you should include several lags of log(1+Volume) as Inputs] 3. Next, for each of the following 

methods and models, produce ‘out-of-sample’ forecasts using either ‘5-fold’ or (ideally) ‘Out-of-Bag’: Linear Regression (w/Ridge or Lasso), 

Logistic Regression (‘Up’/’Down’), Support Vector Machine, Gradient Boosting Other method of your choice (preferably from HW) Each group 

member should take the lead on one of the methods above, which should be clearly indicated. 4. Include the ‘Expert’ forecasts from the above alongside

 the original input variables and run a Deep (3 or more hidden layer) Neural Network to forecast return from the combined inputs. Use the (m(x)y-1)^2 loss

 function as discussed in class. 5. Perform an analysis of trading performance of the final Stacked Ensemble model using (a) ‘Long-Short’ investing and (b) 

‘Proportional’ (to forecast) investing, and present cumulative P/L plots and (annualized) Sharpe Ratios, highlighting the performance on (‘out-of-sample’) ‘testing’ data.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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